| Sumario: | Artificial intelligence (AI) has the potential to transform wound care by addressing inconsistencies in assessment, clinical inefficiencies, and alleviating resource constraints in a speciality that imposes significant economic burden on healthcare systems. This article explores AI's applications, evidence and future directions in wound management. It reviews core AI methodologies -- machine learning, deep learning, and natural language processing -- and how they are driving innovations including computer vision for wound imaging, predictive analytics for healing trajectories, and smart dressings for real-time monitoring. These technologies can enhance diagnostic accuracy, standardise assessments, and enable early detection of complications, supporting personalised treatment strategies. With this prospective step change in our approach to wound care, challenges persist, including infrastructure needs, data privacy concerns, bias in AI imaging with different skin tones, workforce training requirements, and financial investment barriers. Successful integration requires alignment with clinical workflows, adherence to ethical standards, and unwavering focus on patient safety. It is crucial that AI is designed and seen to augment rather than replace clinical expertise, highlighting the need for ethical governance and ongoing evaluation. By balancing technological innovation with clinical excellence, AI can enhance patient outcomes, optimise resource allocation, and maintain high standards in wound care. Realising AI's full potential will depend on collaboration among clinicians, researchers, and policymakers to build resilient, patient-centred healthcare systems.
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